Socioeconomic factors associated with cessation of injection drug use among street-involved youth
Bibliographic record
Abstract
BACKGROUND: Although the initiation of injection drug use has been well characterized among at-risk youth, factors that support or impede cessation of injection drug use have received less attention. We sought to identify socioeconomic factors associated with cessation of injection drug use among street-involved youth. METHODS: From September 2005 to May 2015, data were collected from the At-Risk Youth Study (ARYS), a prospective cohort study of street-involved youth in Vancouver, Canada. Multivariate extended Cox regression was utilized to identify socioeconomic factors associated with cessation of injection drug use for six months or longer among youth who were actively injecting. RESULTS: Among 383 participants, 171 (44.6%) youth reported having ceased injection (crude incidence density 22 per 100 person-years; 95% confidence interval [CI], 19-26) at some point during study follow-up. Youth who had recently dealt drugs (adjusted hazard ration [AHR], 0.50; 95% CI, 0.29-0.87), engaged in prohibited street-based income generation (AHR, 0.41; 95% CI, 0.24-0.69), and engaged in illegal income generating activities (AHR, 0.19; 95% CI, 0.06-0.61) were significantly less likely to report cessation of injection drug use. CONCLUSIONS: Our findings suggest that socioeconomic factors, in particular engagement in prohibited street-based and illegal income generating activities, may pose barriers to ceasing injection drug use among this population. Effort to improve access to stable and secure income, as well as employment opportunities may assist youth in transitioning away from injection drug use. TRIAL REGISTRATION: Our study is not a randomized controlled trial; thus the trial registration is not applicable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".